The present invention relates to the detection and evaluation of multiple imbalances within multi-parametric systems, particularly to the employment of graphic means for performing such detections and evaluations. It is particularly useful in the field of medicine for the diagnosis and follow-up treatment of diseases. It may be used in many other fields for evaluating, diagnosing, predicting, analyzing, describing behavior, change of behavior, etc., where multiple parameters in a related system are involved.
In medicine, for optimal care and therapy, quantitative as well as qualitative judgments of the degrees of abnormalities should be made when diagnosing patients. Previous studies have suggested that an analysis of combinations of laboratory data of a patient may be of greater aid in understanding the patient""s condition than an analysis of individual items of data per se.
Heretofore one scientific method of diagnosing diseases from laboratory data has used a statistical analysis of deviations of a patient""s data from a normal range. The results obtained were arranged in the form of a circular coordinate system which employed radial axes calibrated according to the patient""s laboratory parameters, with standard deviations of each parameter plotted on the respective axes. Following this, a pattern was created by interconnecting adjacent points on the axes. Diagnosis was performed by comparing the obtained pattern of an individual patient with reference patterns typical for certain diseases. J. H. Siegel, xe2x80x9cRelations Between Circulatory and Specific Changes in Sepsis,xe2x80x9d 32 Ann. Rev. Med. (Annual Reviews, Inc. 1981) 175-194; also see the xe2x80x9cPatient Data System,xe2x80x9d General Electric Medical Systems (adv""t.), Critical Care Medicine, January/February 1976.
While useful, these methods did not provide sufficient information for one to detect pathology with normal data and did not reveal qualitative and quantitative types of imbalances between parameters.
Another method has been suggested in an attempt to overcome these difficulties. This method was similar to the previous ones: a circular type representation of parameters on radial axes was provided with values plotted on the radial axes, but expressed as a percentage of normal values, rather than by standard deviations. S. Nazari et al., xe2x80x9cA Multivariable Pattern for Nutritional Assessment,xe2x80x9d 4 J. Parenteral and Enteral Nutrition 499, 1980.
This method provided more distinguishable patterns than the previous one because the percentage scale was more sensitive than the standard deviation scale. Nevertheless this method still did not provide sufficient information for one to obtain quantities and qualitative types of imbalances between parameters and did not reveal any multiple imbalances which were present within the system.
In order to overcome disadvantages of the aforementioned known methods and systems, the applicant have developed a new diagnostic system based on so-called balascopic units which is described in U.S. Pat. No. 4,527,240 issued to the applicant in 1985 and incorporated herein by reference. In this system, relationships between multiple related parameters, such as blood chemistry data, are evaluated by converting the data into specially normalized units as a percentage on a scale depicting the maximum and minimum empirical values for such parameter.
The essence of the balascopy consists of transformation of measured values into dimensionless balascopic scales. The balascopic units used in these scales are dimensionless and are based on the following assumptions.
Let us assume that Pmax is the maximum value ever measured for a certain parameter, e.g., serum protein and that that Pmin is the minimum value ever measured for serum protein. These values are obtained from the existing data based on large amounts of available measurements. Let us designate the difference between Pmax and Pmin as "xgr", i.e.,
Pmaxxe2x88x92Pmin="xgr"=Const (mg/dl) 
Let us introduce an inverse scale based on 100 units based on the following transformation:
100/"xgr"=xcex7=Const (dl/mg). 
The measured value of the parameter is designated as Pmes (mg/dl). The same parameter obtained from the available statistical data for healthy people is designated as Pmes (st.norm).
Let us subtract Pmin from Pmes and designate the result of subtraction as xcexd, i.e.,
Pmesxe2x88x92Pmin=xcexd(mg/dl) 
It is understood that for a live person u is always greater than 0. A balascopic unit Pbu, on which the previous and the present invention of applicant are based, is equal to:
Pbu=(100/"xgr")xc2x7xcexd(dimensionless) 
Pbuis always less than 100.
It is obvious that the value of Pbu corresponding to xcexdnorm=Pmes(st. norm)xe2x88x92Pmin, i.e., Pbu norm.=(100/"xgr")xc2x7xcexdnorm is an average statistical value of a selected parameter for a healthy person, e.g., of serum protein, expressed in balascopic units.
A main advantage of transfer to balascopic units is the use of large statistic data obtained for healthy and unhealthy people (Pmax and Pmin).
For example, the empirically existing maximum of the total serum protein in vivo comprises 11.0 milligrams (mg) of protein per tenth liter (deciliterxe2x80x94dl) of blood, and the minimum is 2.0 mg/dl. The range between these values is thus 11.0-2.0=9.0 mg/dl. This range is then converted into special normalized units on a scale of 100, such that each normalized unit will correspond to 100/9=11.1 actual units (in mg/dl). A patient""s measured total serum protein value may be thus converted to normalized units by subtracting the minimum actual value from the patient""s actual value and then multiplying the result by 11.1 or by 100/9.
For example, if a patient""s measured total serum protein is 7.3 mg/dl, this value is made the minuend, the minimum empirical value (2.0 mg/dl), is made the subtrahend, and the difference, 5.3 mg/dl, is determined. This difference (5.3 mg/dl) is then multiplied by the normalized unit value, 11.1, to provide a special normalized value according to the invention, which is 58.9 units.
The applicant have designated these special normalized units (regardless of the parameter represented) by the term Balascopic units, where xe2x80x9cbalaxe2x80x9d stems from the word xe2x80x9cbalancedxe2x80x9d and xe2x80x9cscopicxe2x80x9d stems from the Greek word xe2x80x9cobservexe2x80x9d. Thus it is clear that the balascopic method is based on visual representation of deviation from balance.
Then a normal relationship between pairs of such data (FIG. 1xe2x80x94N) is provided and compared with measured relationships between corresponding pairs of data (FIG. 1xe2x80x94CN to FN) and quantitative and qualitative evaluations are made. Also the complete set of data for such a system is plotted on respective radial axes in such normalized units on a circular coordinate system with the respective maximum and minimum for each parameter being marked on its radius. This is shown in FIG. 2 which is a circular diagram of a balascopic pattern for blood chemistry. This diagram contains a closed-loop contour 22 plotted for maximum values of the parameters and a closed loop contour 21 for minimum values of the parameters, while a normal closed-loop pattern, which is completely within the area defined between the contours 20 and 21 and which is Pbu st.norm, is an average statistical value for normal parameters of a healthy person (FIG. 2). Then measured parameters for various entities are similarly plotted and compared with the normal annulus or known abnormal annuli (FIG. 3 is a diagram of the type shown in FIG. 2 for Diabetes mellitius with Kimmel Stiel-Wilson disease and Secondary Hyperparathyroidism. It is understood that similar diagrams can be plotted for other diseases such as myxedema, thyrotoxicosis, etc. with deviation patterns typical of each specific disease.
Generally, laboratory data or measured parameters of a patient are used to make and confirm a diagnosis and to monitor the course of treatment. In a basic aspect of the present invention, each measured parameter of a patient is noted and is made far more useful and meaningful by expressing it as a percentage between the minimum and maximum empirical values of said parameter.
Given below are some explanations and definitions given in U.S. Pat. No. 4,527,240 and repeated here as they will be used in the present patent application.
In FIG. 1, the vertical scale is calibrated in Balascopic units (BU) from 0 to 100, with 0 BU corresponding to the existing empirical minimum and 100 BU corresponding to the existing empirical maximum of both total serum protein and serum albumin of a patient. The first (leftmost) block of this diagram (labeled Normal and N) shows how Balascopic units can be used to represent a normal relationship between these two blood parameters. In this block, point PN represents a normal value of total serum protein in a patient. The absolute value of Example 1 when converted from mg/dlxe2x80x94not indicatedxe2x80x94to Balascopic units, gives 58.9 or 59 BU, as explained.
Assume further that the normal patient""s serum albumin is measured in an absolute measurement (not indicated) and when converted to Balascopic units (according to the above-described method), is 70 BU. This parameter is indicated at point AN.
A broken line is drawn to connect points PN and AN; this line indicates the normal relationship between these two parameters. A normal differential (sometimes called xe2x80x9cgradientxe2x80x9d) between total serum protein (PN) and serum albumin (AN) is thus equal to 70 BU-59 BU=11 BU. Preferably block N is made of transparent material so that it can be superimposed over any other block in FIG. 1.
In FIG. 1, the second block from the left, CN, illustrates a deviation from the normal relationship between total serum protein and serum albumin. In this case the values are Closer than Normal (CN); this closer-than-normal relationship is sometimes called an xe2x80x9cintegratedxe2x80x9d relationship. According to the previous procedure, the two values are measured, converted to BU, and the resultant points are connected. The resultant differential between them is assumed equal to 5 BU, i.e., less than the normal differential (Block N) of 11 BU.
Since the two values P and A in blocks N and CN are plotted the same distance apart, the gradients of their interconnection lines can be easily compared by a superimposition of the normal gradient upon the actual measured gradient in block CN. The abnormality of the patient in block CN can easily be seen by the reduced slope of the solid gradient line PCNxe2x80x94ACN in this block when compared with the normal gradient (broken line PNxe2x80x94AN).
Similarly, a type of imbalance where the two parameters are too far apart in shown in the next block, FN (Further than Normal). Here the Balascopic differential is equal to 40 BU, which is farther than the normal 11 BU differential. This xe2x80x9cFNxe2x80x9d (sometimes called xe2x80x9cdisintegratedxe2x80x9d) relationship can easily be seen by the increased slope of the interconnection line PFNxe2x80x94AFN, especially when compared with the superimposed normal gradient (broken line PNxe2x80x94AN) superimposed thereover.
In the next block (NI), line PNIxe2x80x94ANI, has a normal Balascopic gradient of 11 BU, but the mutual positions of the two points are inverted from normal. This type of relationship is called Normal Inverted (NI) and also is vividly demarcated by the superimposed broken line PNxe2x80x94AN.
In the next block CI (Closer and Inverted) (sometimes called xe2x80x9cintegratedxe2x80x9d and inverted), line PCIxe2x80x94ACI represents a close-than-normal and inverted relationship with a Balascopic gradient of 10 BU. Compare this line with the normal broken line PNxe2x80x94AN. In the last block, FI, a Farther-than-Normal and at the same time inverted relationship is shown by the line PFIxe2x80x94AFI. This Frther-than-Normal and inverted (sometimes called xe2x80x9cdisintegratedxe2x80x9d and inverted) relationship is also vividly demarcated by the superimposed normal broken line PNxe2x80x94AN.
As will be recognized by those skilled in the art, the above method reveals five new definitive and qualitative types of imbalances between blood chemistry parameters that can be established. This method can also be used for any given pair of parameters in a system of related parametric quantities. Each of the above imbalances can be quantitatively estimated by the degree of imbalance in percent.
The relationships between many parameters in a system or related parameters can be represented simultaneously by the method illustrated by the diagram of FIG. 2. This drawing shows a circular coordinate system having twelve radial lines corresponding to twelve standard blood chemistry parameters, 1. albumin, 2. Ca++ (Calcium ions), 3. phosphorus, 4. AST(SGOT) (serum glutamic oxytransaminase), 5. glucose, 6. alkaline phosphatase (ALK.PHOS.), 7. LDH (lactic dehydrogenose), 8. bilirubin total, 9. BUN (blood urea nitrogen), 10. uric acid, 11. cholesterol, and 12. total protein. The reference or normal range values for these parameters are plotted in normalized or Balascopic units (BU) on the respective axes in the manner aforedescribed. The mean values of these parameters are then interconnected to form a closed or endless line 20.
The shaded ring-shaped or annular area 22 in FIG. 2 shows the normal range for a healthy population chosen by conventional statistical methods. Area 22 is drawn by plotting the normal lowest and highest values for each parameter on its radial axis, and then interconnecting the lowest points and the highest points to form two closed lines (similar to line 22) and shading the area between these lines. Note that the parameters connected by line 20 all fall within the normal range. In order to simplify the visual comparison and present it in a more obvious way, the radial axes on the circular diagrams of FIG. 2 are arranged in the specific order indicated (rather than the standard sequence of a laboratory test routine) so that the boundaries limiting the normal range will define the substantially annular pattern shown. If the axes were arranged in an order corresponding to the sequence of a standard laboratory test routine, the pattern of the normal range would have been too complicated for comparison and too difficult to employ as an effective and in diagnosis.
FIG. 3 is a similar circular diagram depicting abnormal patterns of blood chemistry typical for a patient with diabetes mellitus with Kimmelstiel-Wilson disease and secondary hyperparathyroidism plotted as line 30.
It is understood that similar abnormal conditions can be presented in the same system for other diseases. It can be seen that the use of a circular diagram with the normal range for the blood chemistry parameters plotted as a shaded ring and the patient""s parameters plotted thereover or hereunder by a solid line greatly facilitates, strengthens, and improves diagnosis, especially when prototype patterns for typical diseases (such as shown in FIG. 3) are superimposed on the diagram, either with (not shown), or in lieu of the normal annular shaded area of FIG. 2.
It is now possible and desirable to obtain a full set of the existing relationships between all parameters of blood chemistry expressed in terms of Balascopic differences or gradients. This can be seen in FIG. 4, where the blood chemistry data from a recently measured patient with a myocardial infarction (MI) and their corresponding values in Balascopic units (BU) are presented and taken from Table 1 below.
By way of example, consider the first line of Table 1 which shows how this patient""s albumin, measured as 4.2 mg/dl, is converted to Balascopic units (BU). The lowest value of albumin measured in a living patient is 1.0 mg/dl. The highest value is 6.0 mg/dl. According to the principle of Balascopy, the difference between these maximum and minimum is taken to be 100 BU. To convert the patient""s actual value of 4.2 mg/dl into BU, subtract the existing minimum (1.0) from the actual value (4.2) multiply the result by 100 divide by the difference between the existing maximum and the existing minimum (5.0) to obtain the value indicated in the rightmost column, 64 BU.
The other blood parameters for this patient have also been processed in this manner to obtain the data in the xe2x80x9cBUxe2x80x9d column.
Note that Table 1 above presents relatively little readily-understandable information and is difficult to analyze or evaluate, either initially, or on a follow-up monitoring, while the chart of FIG. 3 presents a readily-identifiable portrayal of the patient""s pathology.
While FIG. 3 vividly depicts the patient""s quantitative relationships, the chart of FIG. 4 shows the entire spectrum of all existing parametric relationships in quantitative as well as qualitative terms, and also indicates the actual and Balascopic units for each parameter. For example in the parameter row (third row down, just above double line) parameter 1 has an actual value (second row down) of 4.2 mg/dl and a value in BU (top row) of 64 units. The parameters are also indicated by number in the rightmost column, to the right of the double line.
The qualitative relationship of parameter 1 (rightmost column) with parameter 10 (third row down) is indicated to be closer than normal by the legend xe2x80x9cCNxe2x80x9d in the block at the intersection of parameters 1 and 10 and this relationship quantitatively is a 22 percent imbalance (same block).
These qualitative and quantitative relationships can be determined as follows: The Balascopic difference between parameter 1 (64 BU) and parameter 10 (39 BU) is 64xe2x88x9239=25 BU. The average Balascopic difference between these two parameters for the healthy population is 48.2 BU, with a standard deviation (SD) of 8.1 BU. Thus the Balascopic difference for a normal relationship (about 95% of the population) should lie between the limits of the average value (xe2x80x9cXxe2x80x9d) xc2x12 SD. Since X is 48.2 BU, this range extends for 48.2xc2x1(2xc3x978.1) BU or 32.0 to 64 BU.
This means that any value of Balascopic difference for a patient""s parameters 1 and 10 between 32.0 and 64.4 BU can be considered a normal relationship.
In the present example, the Balascopic difference is only 25 BU, which is less than 32.0 BU and therefore is an imbalance type of relationship, a CN (Closer than Normal) type because the difference (25 BU) is less than the normal difference.
To evaluate the quantitative degree of closeness, consider the range between the lower limit of the normal difference (32 BU) and the maximum closeness (0 BU) as 100% and determine the degree of actual closeness in percent.
In the present case the lower limit of normal difference (32 BU) less the actual difference (25 BU) is divided by the lower limit (32) times 100=22 percent, as indicated in FIG. 7 in the block at the intersection of parameters 1 and 10.
The formulae above used are valid for all of the abnormal qualitative relationships indicated in FIG. 1 (CN, FN, CI, and FI).
The following Table 2 is a statistical analysis of the data in Table 1 and FIG. 4. This table shows, for the 66 existing pairs of relationships of the 12 blood parameters used, the actual number of occurrences of each type of relationship, the percentage of the total for each type of relationship, the statistical mean, in BU, of each type of relationship, and the statistical coefficient of variation for each relationship.
As will be appreciated by those skilled in the art, it is difficult to draw a conclusion from data presented in the above tabulation, but by processing the data and presenting it in the form of the analytical graphs with superimposed normal or known condition range values, a far clearer picture of pathology is readily presented.
FIG. 5 presents another method of graphically portraying the relationships and more vividly indicating the degree of abnormality. FIG. 5 is divided into six parts, FIGS. 5A to 5F, respectively showing the six types of specific relationships (N, CN, FN, NI, CI, and FI), as discussed for the patient with the myocardial infarct whose data are not shown but also can be presented similar to the form of FIG. 3.
Each part of FIG. 5 has 12 dots, numbered 1 to 12, spaced evenly in a circular configuration, each dot representing one of the 12 blood chemistry parameters aforediscussed. In each part of FIG. 5, every pair of parameters which have the specific relationship specified by the heading of the part is indicated by a line interconnecting the pair of dots representing the parameters which have such a specific relationship. Thus in FIG. 5A, the dots for every pair relationship which have a normal relationship, i.e., the relative values of the parameters are in the normal relationship range, are interconnected by a line. In the patient under consideration, the relative values for the following parameters are in the normal range and hence the following pairs of dots are joined in FIG. 5A: 1-2, 1-3, 1-6, 1-8, 1-9, 1-11, 1-12, 2-3, 2-8, 2-9, 2-12, 3-5, 3-6, 3-8, 3-9, 3-11, 3-12, 5-7, 5-10, 5-11, 5-12, 6-8, 6-9, 6-11, 6-12, 7-10, 7-11, 8-9, 8-11, 8-12, 9-11, 9-12, and 11-12.
Since 50% of all existing pairs of parameters are joined in FIG. 5A, this is indicated by the legend xe2x80x9cN: 50%xe2x80x9d meaning that 50% of the parametric relationships for this patient are normal. Obviously the more lines that are present in a xe2x80x9cnormalxe2x80x9d diagram (FIG. 5A), the better the patient""s blood chemistry condition.
In FIG. 5B, on the other hand, lines are shown for only the parametric relationships which are abnormal in the normal but inverted (NI) manner. Since this a pathalogical condition, obviously the more lines, which are present in FIG. 5B (as well as FIGS. 5C to 5F), the worse the patient""s condition. As indicated in FIG. 5B, 12% of the patient""s blood chemistry parametric relationships are normal-inverted (NI).
The remaining four sections, C, D, E, and F, of FIG. 5 represent the CN, CI, FN, and FI abnormal relationships and the percentages of each of these abnormal of these abnormal relationships is indicated. In each of these sections, the mean degree of abnormality (in BU) of the represented abnormal parameter is also indicated, as is the statistical coefficient of variation (CV) of such abnormal parameters. (No mean or CV is indicated in FIG. 6A or 6B because these sections represent parametric relationships with quantitatively normal values).
It will be appreciated that the charts of FIG. 5 will present significantly more comprehendible information to a trained person than numerical data alone, or prior art charts. Also follow up evaluation is greatly facilitated by comparing charts for a patient at sequential stages of a disease.
In fact, in U.S. Pat. No. 4,527,240 considers four tools suitable for graphical representation and analysis of multiple parameters in their interrelationships. More specifically, the first tool was described with reference to FIG. 1 and may be named xe2x80x9canalysis of pairsxe2x80x9d; the second tool was described with reference to FIGS. 2 and 3 and may be names xe2x80x9ccircular diagram methodxe2x80x9d; the third tool was described with reference to FIG. 4 and may be names xe2x80x9cmatrix representation methodxe2x80x9d; and the fourth method was described with references to FIG. 5 and may be named xe2x80x9cmultiple relationship graphs methodxe2x80x9d.
Each of the aforementioned tools, may be used individually or in combinations. However, these tools still possess a number of disadvantages which does not allow to use to full extent the potentials inherent in statistical data. A main disadvantage of the first tool, which has been described with reference to FIG. 1, is that it does not present a sufficient quantitative information required for evaluation of relationships between the interconnected pairs of parameters.
Even though the graphs of FIG. 1 have scales, it is not convenient to use these scales for quantitative evaluation. Furthermore, this method is time-consuming and is not sufficiently illustrative.
The circular diagrams shown in FIGS. 2 and 3 are not sufficiently convenient for distinguishing between various diseases because of great diversity of shapes of polygons formed by connecting points on the radial axes. Even those polygons which are plotted for one and the same disease may exist in such great variety that it would difficult to reveal an actual disease with sufficient accuracy. Thus, in spite of certain usefulness, the method based on graphical representation in a circular coordinate system cannot be used along and, as a rule, require combination with one or both other methods.
An advantage of the matrix representation method of FIG. 4 is that it may contain a sufficient quantitative information, including the statistic data introduced through the use of balascopic units. However, it is obvious that this method is not sufficiently illustrative graphically as it shows only the numbers.
Finally, the fourth tool, i.e., the multiple relationship graphs method, which demonstrated normal or abnormal specific relationships between many parameters in the form of a planar graph, is purely qualitative and does not contain any quantitative information.
In view of the above disadvantages, the existing balascopic methods are not sufficiently sensitive for accurate diagnosing, and the four tools described in the previous patent of the applicant diseases should be used only in combination of at least two, and preferably of all four. It is understood that for using the advantages of the balascopic approach to full extent, the above methods need to be modified and improved.
It is an object of the present invention to provide a balascopic method and system which combine two or more of the previous system analysis tools in one. Another object is to improve each existing balascopic tool individually. Still another object is to improve sensitivity of the balascopic methods in diagnosing diseases. A further advantage is to improve informativity of the balascopic method simultaneous with simplification of representation methods. Another object is to provide a balasopic method and system for presenting a qualitative, quantitative, and pictorial information in a single graph.
The method and system of the present invention improve and further develop the balascopic concept disclosed in U.S. Pat. No. 4,527,240. The xe2x80x9canalysis-of-pairxe2x80x9d method is improved and simplified by replacing the graphical linear representation of FIG. 1 by so-called balascopic vectors, which show a direction of changes of the relationship from normal and the length of which corresponds to the amount of the change. The normal relationships are expresses by scalar values in the form of vertical linear sections, while deviations from the normal are expressed by vertical vectors having lengths corresponding to the magnitude of the deviation. The circular diagram method is improved by dimensisonlessly resealing the balascopic unit of FIGS. 2 and 3 into a system in which deviations for all parameters are shown from mean statistic values recalculated on the same radius of the circular diagram. The diagrams show three substantially concentric circles, of which the inner circle corresponds to the minimal normal values, the outer circle corresponds to the maximal normal values, and the intermediate circle corresponds to the mean statistic values. The latter are assumed as 100% normal value of corresponding parameters. With the use of such converted system, it becomes possible to present all deviations of diagnostic parameters from normal condition in more visually obvious form and in a relative balascopic units, hereinafter referred to as xe2x80x9crelative balascopic unitsxe2x80x9d. According to another embodiment for balascopic representation of diagnostic data, the circular diagrams of the invention are developed into a linear form which is more convenient for observing the dynamics of the disease by arranging the graphs plotted at time intervals, one beneath the other.
The matrix representation method is improved by rearranging the parameters into a conventional orthogonal matrix, wherein the diagonal cells contain balascopic units combined with values of the parameters measured in natural units of these parameters, and wherein other cells contain values and symbols that characterize magnitude of the change in relationships in comparison with normal relationships. The invention also improves the multiple relationship graphs by showing not only links between all normal and abnormal parameters with specific relationships, but also the sign of the change relative to normal change in terms of vectors, while the length of the vector shows the absolute value of the change.